The Ultimate Data Set


Until recently, using entire populations as data sets was impossible—or at least impractical—given limitations on data collection processes and analytical capabilities. But that is changing.

The emerging field of computational social science takes advantage of the proliferation of data being collected to access extremely large data sets for study. The patterns and trends in individual and group behavior that emerge from these studies provide “first facts,” or universal information derived from comprehensive data rather than samples.

“Computational social science is an irritant that can create new scientific pearls of wisdom, changing how science is done,” says Brian Uzzi, a professor of management and organizations at the Kellogg School. In the past, scientists have relied primarily on lab research and observational research to establish causality and create descriptions of relationships. “People who do lab studies are preoccupied with knowing causality,” Uzzi says. “Computational work says, “I know that when you see X, you see Y, and the reason why that happens may be less important than knowing that virtually every time you see X, you also see Y.”

“Big data goes hand in hand with computational work that allows you to derive those first facts,” Uzzi says. “Instead of trying to figure out how scientists come up with great ideas by looking at 1,000 scientists, you look at 12,000,000 scientists—potentially everyone on the planet. When you find a relationship there, you know it’s universal. That universality is the new fact on which science is being built.”


Computation in the Social Sphere

Studying large data sets for first facts about human behavior has led to striking advances in recent years. Uzzi notes how one particular data set—mobile-phone data—“has taught us very distinctively about human mobility and its implications for economical and social stratification in society.” It has also shed light on how people behave during evacuations and emergency situations, including infectious-disease outbreaks. Knowing how behaviors affect the spread of diseases can help public health officials design programs to limit contagion.

The ability to track the social behavior of large groups has also shifted people’s understanding of human agency. “Until recently, we really believed that each of us made our decisions on our own,” Uzzi says. “Our friends may have influenced us here or there but not in a big way.” But troves of social-media data have shown that people are incredibly sensitive and responsive to what other people do. “That’s often the thing that drives our behavior, rather than our own individual interests or desires or preferences.”

This may change how you think about your consumer behavior, your exercise regimen, or what you Tweet about. Researchers like Uzzi are also deeply interested in how this responsiveness influences political behavior on larger issues like global climate change or investments in education systems. Think of it as a shift from envisioning yourself as a ruggedly individual, purely rational, economic person to a sociological person who encounters and engages and decides in concert with others.

One aspect of computational social science—brain science—has already discovered that those decisions are often being made before we even know it. “Brain science has taught us a lot about how the brain reacts to stimuli,” Uzzi says. With the visual part of your brain moving at roughly 8,000 times the speed of the rest of your brain, the visual cortex has already begun processing information—and leaping to certain conclusions—before the rest of your brain ever catches up. And with 40 percent of the brain’s function devoted strictly to visualization, “if you want to get in front of anything that’s going to lead to a decision, an act of persuasion, an in-depth engagement with an idea, it has got to be visual.”

“The really big things are understanding how something diffuses through a population and how opinions change,” Uzzi says. “If you put those two things together, you really have an understanding of mass behavior.”

This increased understanding of both mass and individual behavior presents huge opportunities for businesses, notably in the health sphere. “There is going to be an entirely new ecology of business that goes beyond how we think about health today,” Uzzi says. “For many people, there is no upper threshold on what they will pay for good health and beauty. With health increasingly decentralized to the individual, that’s going to spin off to companies that want to take advantage of this information to help people do things better.”

Scaling from One to Everyone

While gathering data on groups as large as the entire population is beneficial to scientists, marketers, and the like, computational social science has the scalability to allow for practical data generation on an individual level as well. This means that you can be the subject of your own data-rich computational study, without control groups or comparison testing. “You actually generate enough data on yourself, every day, that could be collected, that you can be the subject of a computational study,” Uzzi says.

Developments in the ability to collect and parse data on individuals is one area where computational social science has the potential to transform people’s lives—from providing more information about individuals’ own health to raising their awareness of unconscious biases to showing how their decision-making processes are influenced by others. “It’s going to allow people to personally use data that can help them improve their lives in a way that they never imagined before,” Uzzi says.

For example, using wearable technologies allows for sensor data collection that can include emotional activation and heart-rate monitoring in social interactions, caloric intake, biorhythms, and nervous energy. The crunching of that raw data into actionable information will happen through our machines. If you think you have a close connection to your smartphone and your tablet now, wait until you rely on it to tell you how much that last workout helped—or did not help—you shake off the tension of a long day at the office.

“Our closest partnership in the world is probably going to be our machine that helps us manage all this,” Uzzi says. This can be transformative by making us healthier.

It may make us less discriminatory, too. We all have cognitive biases that lead us to make irrational decisions. These are thought to be hard-wired things we can identify but not necessarily change on our own. Sensor data can provide a feedback loop of how we have acted in the past. This has the potential to improve future decision making. If your sensors pick up signals that show your body acting differently around certain groups, perhaps in ways that you suppress or to which you are oblivious, that may be harder to ignore.

“Our own sense of identity could be greatly shaken by this, or improved, or both.”

Source: Kellogg Insight

What Is Machine Learning???

Machine Learning for Dummies


Amazon uses it. Target uses it. Google uses it. “It” is machine learning, and it’s revolutionizing the way companies do business worldwide.

Machine learning is the ability for computer programs to analyze big data, extract information automatically, and learn from it. With 250 million active customers and tens of millions of products, Amazon’s machine learning makes accurate product recommendations based on the customer’s browsing and purchasing behavior almost instantly. No humans could do that.

Target uses machine learning to predict the offline buying behaviors of shoppers. A memorable case study highlights how Target knew a high school girl was pregnant before her parents did.

Google’s driverless cars are using machine learning to make our roads safer, and IBM’s Watson is making waves in healthcare with its machine learning and cognitive computing power.

Is your business next? Can you think of any deep data analysis or predictions that your company can produce? What impact would it have on your business’s bottom line, or how could it give you a competitive edge?

Why Is Machine Learning Important?

Data is being generated faster than at any other time in history. We are now at a point where data analysis cannot be done manually due to the amount of the data. This has driven the rise of MI — the ability for computer programs to analyze big data and extract information automatically.

The purpose of machine learning is to produce more positive outcomes with increasingly precise predictions. These outcomes are defined by what matters most to you and your company, such as higher sales and increased efficiency.

Every time you search on Google for a local service, you are feeding in valuable data to Google’s machine learning algorithm. This allows for Google to produce increasingly more relevant rankings for local businesses that provide that service.

Big Big Data

It’s important to remember that the data itself will not produce anything. It’s critical to draw accurate insights from that data. The success of machine learning depends upon producing the right learning algorithm and accurate data sets. This will allow a machine to obtain the most efficient insights possible from the information provided. Like human data analysts, one may catch an error another could potentially miss.

Digital Transformation

Machine learning and digital technologies are disrupting every industry. According to Gartner, “Smart machines will enter mainstream adoption by 2021.” Adopting early may provide your organization with a major competitive edge. Personally, I’m extremely excited by the trend and recently spent time at Harvard attending its Competing on Business Analytics and Big Data program along with 60 senior global executives from various industries.

Interested In Bringing The Power Of Machine Learning To Your Company?

Here are my recommendations to get started with the help of the right tools and experts:

  1. Secure all of the past data you have collected (offline and online sales data, accounting, customer information, product inventory, etc.). In case you might think your company doesn’t generate enough data to require machine learning, I can assure you that there is more data out there than you think, starting with general industry data. Next, think about how you can gather even more data points from all silos of your organization and elsewhere, like chatter about your brand on social media.
  2. Identify the business insights that you would benefit from most. For example, some companies are using learning algorithms for sales lead scoring.
  3. Create a strategy with clear executables to produce the desired outcomes such as fraud protection, higher sales, increased profit margin and the ability to predict customer behavior. Evaluate and revisit this strategy regularly.

Source: Forbes

5 Questions to Assess Digital Transformation at the Enterprise Level


Digital transformation is still one of the business buzzwords of the year. It is estimated that 89% of organizations have digital transformation as a business priority. But if you feel like you’ve come to a standstill in your digital transformation efforts, you are not alone. As many as 84% of digital transformation efforts fail to achieve desired results. And that statistic would likely be higher if we examined only the larger, enterprise level efforts.

What exactly is digital transformation? According to researchers at MIT Sloan, digital transformation occurs when businesses are focused on integrating digital technologies, such as social, mobile, analytics and cloud, in the service of transforming how their businesses work. The preoccupation with digital transformation makes sense given the pace of change. Richard Foster, at the Yale School of Management, found that the average lifespan of an S&P company dropped from 67 years in the 1920s to 15 years today.

Creating digital products receives a lot of press. For example, the 2017 Ford GT supercar’s digital instrument display has been advertised as the dashboard of the future featuring a state-of-the-art 10-inch digital instrument display that helps reduce driver distraction. Yet, Ford’s share price is down nearly 30% over the past 3 years. On the other hand, the design of the Airbus 380 aircraft had some exciting digital innovations, but Airbus also leveraged big data to improve customer experience with very positive results on the company’s share price over the past 3 years. GE is another example of a company that has pursued digital transformation to reinvent its own industrial operations through digital technology, and then leveraged those learnings to help its customers do likewise. While the product innovations are sometimes impressive, more than purely product related innovations are needed for digital transformation at the enterprise level.

There’s no doubt that the digital tools which includes social, mobile, analytics and cloud (sometimes referred to as the “SMAC” acronym) creates value – but digital transformation at the enterprise level must go beyond just the tools.

Having a transformative purpose or vision and a process based view is recognized as being important. In “Leading digital,” the authors found that firms with a strong vision and mature processes for digital transformation were more profitable on average, had higher revenues, and achieved a bigger market valuation than competitors without a strong vision. Yet more reason to emphasize that while technology is integral to digital transformation – it can’t just be about technology. If we go back to the early days of the research on digital transformation, it was proposed that true digital transformation at the enterprise level needs to embrace fundamental change is three areas: customer experience, operational processes, and business models.

Focusing on customer experience is central to success. According to the Altimeter Group in 2014, around 88% of companies reported undergoing digital transformation – yet only 25% of respondents indicated that they had mapped the customer journey. The 2016 update to this research, based on survey data from 528 leaders, found that the number of companies which mapped customer journey had risen to 54% – indicating a positive trend – but still a way to go.

Focusing on improving the organization’s ability in improving end to end business processes is also needed for success with digital transformation. Where does your organization stand in terms of its process maturity? Are you just beginning the process improvement and management journey or is the organization well on the way to modeling, improving, measuring and managing its key business processes to achieve business goals? If there is room to improve your people’s skill in areas such as BPM, customer experience and change management, then you may wish to explore the training programs offered on these topics at:

Further, the answers to the following questions may provide you with additional insight on your organization’s situation on its enterprise digital transformation journey:

  1. To what extent is your company strategy driving the digital transformation program?
  2. To what extent are you actively challenging the elements of your business model (i.e. value proposition, delivery channels, etc.)?
  3. To what extent are you exploring new digital business and digitally modified businesses?
  4. To what extent do your leaders have a shared understanding of the entire customer journey?
  5. To what extent are you deploying digital to redesign end to end business processes?

Recall the power of the one page principle. This involves in having a high level schematic – just one page for your customer journey map, one page for your business model, and one page for your process relationship map. That’s what drives discussion and collaboration and storytelling. Of course, some of these high level schematics need to be developed at a more granular level of detail – but the one page view is what captures attention and drives dialogue.

The vast majority of digital transformation efforts at the enterprise level are led from the top. Leading by example is part of the success formula as well as defining clear priorities and managing the cross-functional interdependencies that many digital solutions often involve. Chances for success are amplified when employees believe that their leaders have the skills to lead the digital strategy and understand the major digital trends – and that is augmented with stories.

How can you get started on the journey? The following were some of the tips presented by Gartner at the Program & Portfolio Management Summit (PPM) in Orlando:

• Assess your organization’s appetite for risk taking
• Be introspective
• Introduce innovation into every project
• Find a project that can be monetized with digital
• Engage in experiments and communicate lessons learned

One of the keynotes at the 2017 Gartner PPM also emphasized that digital business is an entirely new game, the rules of which are not yet written. Whatever road you choose for your digital transformation journey, it will be important to take into account the central role of customer experience, the power of process management, and the importance of having clear priorities.

Source: BPM Institute